BigQuery ML models with feature engineering

In this notebook, we will use BigQuery ML to build more sophisticated models for taxifare prediction.

This is a continuation of our first models we created earlier with BigQuery ML but now with more feature engineering.

Learning Objectives

Create and train a new Linear Regression model with BigQuery ML
Evaluate and predict with the linear model
Apply transformations using SQL to prune the taxi cab dataset
Create a feature cross for day-hour combination using SQL
Examine ways to reduce model overfitting with regularization
Create and train a DNN model with BigQuery ML

Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.



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%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT



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import os

PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1

# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGION
os.environ["BUCKET"] = PROJECT # DEFAULT BUCKET WILL BE PROJECT ID

if PROJECT == "your-gcp-project-here":
  print("Don't forget to update your PROJECT name! Currently:", PROJECT)

Create a BigQuery Dataset and Google Cloud Storage Bucket

A BigQuery dataset is a container for tables, views, and models built with BigQuery ML. Let's create one called serverlessml if we have not already done so in an earlier lab. We'll do the same for a GCS bucket for our project too.



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%%bash

## Create a BigQuery dataset for serverlessml if it doesn't exist
datasetexists=$(bq ls -d | grep -w serverlessml)

if [ -n "$datasetexists" ]; then
    echo -e "BigQuery dataset already exists, let's not recreate it."

else
    echo "Creating BigQuery dataset titled: serverlessml"
    
    bq --location=US mk --dataset \
        --description 'Taxi Fare' \
        $PROJECT:serverlessml
   echo "\nHere are your current datasets:"
   bq ls
fi    
    
## Create GCS bucket if it doesn't exist already...
exists=$(gsutil ls -d | grep -w gs://${PROJECT}/)

if [ -n "$exists" ]; then
   echo -e "Bucket exists, let's not recreate it."
    
else
   echo "Creating a new GCS bucket."
   gsutil mb -l ${REGION} gs://${PROJECT}
   echo "\nHere are your current buckets:"
   gsutil ls
fi

Model 4: With some transformations

BigQuery ML automatically scales the inputs. so we don't need to do scaling, but human insight can help.

Since we we'll repeat this quite a bit, let's make a dataset with 1 million rows.



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%%bigquery
CREATE OR REPLACE TABLE serverlessml.feateng_training_data AS

SELECT
  (tolls_amount + fare_amount) AS fare_amount,
  pickup_datetime,
  pickup_longitude AS pickuplon,
  pickup_latitude AS pickuplat,
  dropoff_longitude AS dropofflon,
  dropoff_latitude AS dropofflat,
  passenger_count*1.0 AS passengers
FROM `nyc-tlc.yellow.trips`
# The full dataset has 1+ Billion rows, let's take only 1 out of 1,000 (or 1 Million total)
WHERE ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 1000)) = 1
# placeholder for additional filters as part of TODO 3 later



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%%bigquery
# Tip: You can CREATE MODEL IF NOT EXISTS as well
CREATE OR REPLACE MODEL serverlessml.model4_feateng
TRANSFORM(
  * EXCEPT(pickup_datetime)
  , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
  , CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek
  , CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday
)

# TODO 1: Specify the BigQuery ML options for a linear model to predict fare amount
# OPTIONS()

AS

SELECT * FROM serverlessml.feateng_training_data

Once the training is done, visit the BigQuery Cloud Console and look at the model that has been trained. Then, come back to this notebook.

Note that BigQuery automatically split the data we gave it, and trained on only a part of the data and used the rest for evaluation. We can look at eval statistics on that held-out data:



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%%bigquery
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL serverlessml.model4_feateng)



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%%bigquery
# TODO 2: Evaluate and predict with the linear model
# Write a SQL query to take the SQRT() of the Mean Squared Error as your loss metric for evaluation
# Hint: Use ML.EVALUATE on your newly trained model

What is the RMSE? Could we do any better?

Try re-creating the above feateng_training_data table with additional filters and re-running training and evaluation.

TODO 3: Apply transformations using SQL to prune the taxi cab dataset

Now let's reduce the noise in our training dataset by only training on trips with a non-zero distance and fares above $2.50. Additionally, we will apply some geo location boundaries for New York City. Copy the below into your previous feateng_training_data table creation and re-train your model.

AND
  trip_distance > 0
  AND fare_amount >= 2.5
  AND pickup_longitude > -78
  AND pickup_longitude < -70
  AND dropoff_longitude > -78
  AND dropoff_longitude < -70
  AND pickup_latitude > 37
  AND pickup_latitude < 45
  AND dropoff_latitude > 37
  AND dropoff_latitude < 45
  AND passenger_count > 0

Yippee! We're now below our target of 6 dollars in RMSE. We are now beating our goals, and with just a linear model.

Making predictions with BigQuery ML

This is how the prediction query would look that we saw earlier heading 1.3 miles uptown in New York City.



In [ ]:

    
%%bigquery
SELECT * FROM ML.PREDICT(MODEL serverlessml.model4_feateng, (
  SELECT 
    -73.982683 AS pickuplon,
    40.742104 AS pickuplat,
    -73.983766 AS dropofflon,
    40.755174 AS dropofflat,
    3.0 AS passengers,
    TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime
))

Improving the model with feature crosses

Let's do a feature cross of the day-hour combination instead of using them raw



In [ ]:

    
%%bigquery
CREATE OR REPLACE MODEL serverlessml.model5_featcross
TRANSFORM(
  * EXCEPT(pickup_datetime)
  , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
    
  # TODO 4: Create a feature cross for day-hour combination using SQL  
  , ML.( # <--- Enter the correct function for a BigQuery ML feature cross ahead of the (
      STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
             CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)
  ) AS day_hr
)
OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg') 
AS

SELECT * FROM serverlessml.feateng_training_data



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%%bigquery
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL serverlessml.model5_featcross)



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%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model5_featcross)

Sometimes (not the case above), the training RMSE is quite reasonable, but the evaluation RMSE is terrible. This is an indication of overfitting. When we do feature crosses, we run into the risk of overfitting (for example, when a particular day-hour combo doesn't have enough taxirides).

Reducing overfitting

Let's add L2 regularization to help reduce overfitting. Let's set it to 0.1



In [ ]:

    
%%bigquery
CREATE OR REPLACE MODEL serverlessml.model6_featcross_l2
TRANSFORM(
  * EXCEPT(pickup_datetime)
  , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
  , ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
                            CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr
)
# TODO 5: Set the model options for a linear regression model to predict fare amount with 0.1 L2 Regularization
# Tip: Refer to the documentation for syntax: 
# https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create
OPTIONS() 
AS

SELECT * FROM serverlessml.feateng_training_data



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%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model6_featcross_l2)

These sorts of experiment would have taken days to do otherwise. We did it in minutes, thanks to BigQuery ML! The advantage of doing all this in the TRANSFORM is the client code doing the PREDICT doesn't change. Our model improvement is transparent to client code.



In [ ]:

    
%%bigquery
SELECT * FROM ML.PREDICT(MODEL serverlessml.model6_featcross_l2, (
  SELECT 
    -73.982683 AS pickuplon,
    40.742104 AS pickuplat,
    -73.983766 AS dropofflon,
    40.755174 AS dropofflat,
    3.0 AS passengers,
    TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime
))

Let's try feature crossing the locations too

Because the lat and lon by themselves don't have meaning, but only in conjunction, it may be useful to treat the fields as a pair instead of just using them as numeric values. However, lat and lon are continuous numbers, so we have to discretize them first. That's what ML.BUCKETIZE does.

Here are some of the preprocessing functions in BigQuery ML:

ML.FEATURE_CROSS(STRUCT(features)) does a feature cross of all the combinations
ML.POLYNOMIAL_EXPAND(STRUCT(features), degree) creates x, x^2, x^3, etc.
ML.BUCKETIZE(f, split_points) where split_points is an array



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%%bigquery

-- BQML chooses the wrong gradient descent strategy here. It will get fixed in (b/141429990)
-- But for now, as a workaround, explicitly specify optimize_strategy='BATCH_GRADIENT_DESCENT'

CREATE OR REPLACE MODEL serverlessml.model7_geo
TRANSFORM(
  fare_amount
  , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
  , ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
                            CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday), 2) AS day_hr
  , CONCAT(
     ML.BUCKETIZE(pickuplon, GENERATE_ARRAY(-78, -70, 0.01)),
     ML.BUCKETIZE(pickuplat, GENERATE_ARRAY(37, 45, 0.01)),
     ML.BUCKETIZE(dropofflon, GENERATE_ARRAY(-78, -70, 0.01)),
     ML.BUCKETIZE(dropofflat, GENERATE_ARRAY(37, 45, 0.01))
  ) AS pickup_and_dropoff
)
OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg', l2_reg=0.1, optimize_strategy='BATCH_GRADIENT_DESCENT') 
AS

SELECT * FROM serverlessml.feateng_training_data



In [ ]:

    
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model7_geo)

Yippee! We're now below our target of 6 dollars in RMSE.

DNN

You could, of course, train a more sophisticated model. Change "linear_reg" above to "dnn_regressor" and see if it improves things.

Note: This takes 20 - 25 minutes to run.



In [ ]:

    
%%bigquery
-- This is alpha and may not work for you.
CREATE OR REPLACE MODEL serverlessml.model8_dnn
TRANSFORM(
  fare_amount
  , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
  , CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
           CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS day_hr
  , CONCAT(
     ML.BUCKETIZE(pickuplon, GENERATE_ARRAY(-78, -70, 0.01)),
     ML.BUCKETIZE(pickuplat, GENERATE_ARRAY(37, 45, 0.01)),
     ML.BUCKETIZE(dropofflon, GENERATE_ARRAY(-78, -70, 0.01)),
     ML.BUCKETIZE(dropofflat, GENERATE_ARRAY(37, 45, 0.01))
  ) AS pickup_and_dropoff
)
-- at the time of writing, l2_reg wasn't supported yet.

# TODO 6: Create a DNN model (dnn_regressor) with hidden_units [32,8]
OPTIONS() 
AS

SELECT * FROM serverlessml.feateng_training_data



In [ ]:

    
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model8_dnn)

We really need the L2 reg (recall that we got 4.77 without the feateng). It's time to do Feature Engineering in Keras.

Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.